Traffic congestion is a condition on a road network that occurs as use increases, and is characterized by slower speeds, longer trip times, and increased vehicular queuing. Several travel demand management techniques have been employed to alleviate traffic congestion. For example, HOV (High occupancy vehicle) lanes can be employed to encourage more efficient travel by requiring vehicles to have a minimum number of occupants in order to legally use the HOV lanes, and thus decrease the amount of vehicles on the roads. HOT (High Occupancy Toll) lanes typically involve a road-pricing scheme that provides motorists in a single-occupant vehicle access to the HOV lanes to legally drive in the less congested HOV lane.
Conventionally, the HOV lane enforcement and HOT lane tolling are performed manually by a police patrol visually observing the occupancy state of vehicles. In one prior art approach, a high power near infrared strobe illuminator is employed to automatically detect the number of occupants in the vehicle because of the properties of the windshield and side windows. Unfortunately, such high-powered strobe requires a large amount of time to recharge before the next flash event can occur. This recharge time effectively limits the volume of traffic that can be measured with this illumination approach. In another prior art approach, more expensive fast recharge illuminator or multiple sequenced illuminators may be employed to maintain the traffic volume requirements. The problem associated with this approach is that these types of illuminators tend to have shorter useful life spans, and so need to be changed more often. Unfortunately, changing the illuminator bulb is a costly maintenance activity since a lane closure is required.
Based on the foregoing, it is believed that a need exists for an improved method and system for selecting a target vehicle for occupancy detection utilizing vehicle identification information, as will be described in greater detail herein.